






import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase								=		logging.get_logger(__name__)






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Dict:
							_lowercase							:   Optional[int] 							=   MobileNetVaConfig(layer_norm_eps=0.001				)

							if "_quant" in model_name:
														raise ValueError('Quantized models are not supported.'				)

							_lowercase							:   Optional[int] 							=   re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'     ,     SCREAMING_SNAKE_CASE				)
							if matches:
														_lowercase							:   Dict 							=   float(matches[1]				)
														_lowercase							:   Any 							=   int(matches[2]				)

							# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
							# the usual 1000. The first class (index 0) is "background".
							_lowercase							:   Union[str, Any] 							=   1_001
							_lowercase							:   Optional[int] 							=   'imagenet-1k-id2label.json'
							_lowercase							:   Union[str, Any] 							=   'huggingface/label-files'
							_lowercase							:   Optional[int] 							=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     repo_type='dataset'				)     ,     'r'				)				)
							_lowercase							:   List[Any] 							=   {int(SCREAMING_SNAKE_CASE				) + 1: v for k, v in idalabel.items()}
							_lowercase							:   List[str] 							=   'background'
							_lowercase							:   Any 							=   idalabel
							_lowercase							:   List[Any] 							=   {v: k for k, v in idalabel.items()}

							return config






def    __magic_name__    (	)     ->    Dict:
							_lowercase							:   Union[str, Any] 							=   'http://images.cocodataset.org/val2017/000000039769.jpg'
							_lowercase							:   Union[str, Any] 							=   Image.open(requests.get(SCREAMING_SNAKE_CASE     ,     stream=SCREAMING_SNAKE_CASE				).raw				)
							return im






@torch.no_grad()
def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=False				)     ->    Union[str, Any]:
							_lowercase							:   str 							=   get_mobilenet_va_config(SCREAMING_SNAKE_CASE				)

							# Load 🤗 model
							_lowercase							:   int 							=   MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE				).eval()

							# Load weights from TensorFlow checkpoint
							load_tf_weights_in_mobilenet_va(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							_lowercase							:   Tuple 							=   MobileNetVaImageProcessor(
							    crop_size={'width': config.image_size, 'height': config.image_size}     ,     size={'shortest_edge': config.image_size + 32}     ,     )
							_lowercase							:   List[str] 							=   image_processor(images=prepare_img()     ,     return_tensors='pt'				)
							_lowercase							:   str 							=   model(**SCREAMING_SNAKE_CASE				)
							_lowercase							:   Union[str, Any] 							=   outputs.logits

							assert logits.shape == (1, 1_001)

							if model_name == "mobilenet_v1_1.0_224":
														_lowercase							:   Optional[int] 							=   torch.tensor([-4.1739, -1.1233, 3.1205]				)
							elif model_name == "mobilenet_v1_0.75_192":
														_lowercase							:   str 							=   torch.tensor([-3.9440, -2.3141, -0.3333]				)
							else:
														_lowercase							:   List[Any] 							=   None

							if expected_logits is not None:
														assert torch.allclose(logits[0, :3]     ,     SCREAMING_SNAKE_CASE     ,     atol=1E-4				)

							Path(SCREAMING_SNAKE_CASE				).mkdir(exist_ok=SCREAMING_SNAKE_CASE				)
							print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(SCREAMING_SNAKE_CASE				)
							print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE				)

							if push_to_hub:
														print('Pushing to the hub...'				)
														_lowercase							:   Union[str, Any] 							=   'google/' + model_name
														image_processor.push_to_hub(SCREAMING_SNAKE_CASE				)
														model.push_to_hub(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
							UpperCamelCase								=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="mobilenet_v1_1.0_224",
							    type=str,
							    help="Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.",
							)
							parser.add_argument(
							    "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
							)
							parser.add_argument(
							    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
							)

							UpperCamelCase								=		parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)










'''simple docstring'''







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     0
   while num > 0:
      digit_sum += num % 10
      num //= 10
   return digit_sum







def 						_A      ( UpperCAmelCase = 100						):






   '''simple docstring'''







   A__       =     1
   A__       =     2

   for i in range(2      ,max_n + 1						):
      A__       =     pre_numerator
      A__       =     2 * i // 3 if i % 3 == 0 else 1
      A__       =     cur_numerator
      A__       =     e_cont * pre_numerator + temp

   return sum_digits(UpperCAmelCase						)


if __name__ == "__main__":
    print(f'''{solution() = }''')


